In recent years, Deep Learning has gained popularity in computer vision and pattern recognition primarily due to Deep Convolution Neural Network (DCNN) [1]}.
DCNN includes convolutional layers, fully connected layers, pooling layers for feature extraction, image representation, and dimensionality reduction respectively. DCNN also includes sigmoid or rectified linear units for non-linearity. CNN is based on local receptive fields and shared weights [2]}. The local receptive fields are regions that provide local information and are spread over the image. Each local receptive field constitutes a neuron and shares weights in the hidden layer. This helps to get distinct feature characteristics. Pooling layers that follow convolution layers, down samples and assist in overcoming overfitting by reducing the number of parameters.
Additionally, dropout, wherein hidden neurons are randomly removed, also helps to avoid overfitting. Pre-trained CNN models are trained on [3]} that carries a large number of data samples and 1000 class labels. The transfer learning techniques can help to apply the existing CNN models to problems where a large dataset is not available.
